Uncertainty Awareness in Wireless Communications, Sensing, and Learning
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| I publikationen: | arXiv.org (Dec 18, 2024), p. n/a |
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| Huvudupphov: | |
| Övriga upphov: | , |
| Utgiven: |
Cornell University Library, arXiv.org
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| Ämnen: | |
| Länkar: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3147568270 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147568270 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Wang, Shixiong | |
| 245 | 1 | |a Uncertainty Awareness in Wireless Communications, Sensing, and Learning | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. The incorporation of machine learning further expands WCS's boundaries, unlocking automated and high-quality data analytics, together with advisable and efficient decision-making. Despite transformative capabilities, wireless systems often face numerous uncertainties in design and operation, such as modeling errors due to incomplete physical knowledge, statistical errors arising from data scarcity, measurement errors caused by sensor imperfections, computational errors owing to resource limitation, and unpredictability of environmental evolution. Once ignored, these uncertainties can lead to severe outcomes, e.g., performance degradation, system untrustworthiness, inefficient resource utilization, and security vulnerabilities. As such, this article reviews mature and emerging architectural, computational, and operational countermeasures, encompassing uncertainty-aware designs of signals and systems (e.g., diversity, adaptivity, modularity), as well as uncertainty-aware modeling and computational frameworks (e.g., risk-informed optimization, robust signal processing, and trustworthy machine learning). Trade-offs to employ these methods, e.g., robustness vs optimality, are also highlighted. | |
| 653 | |a Wireless communications | ||
| 653 | |a Modularity | ||
| 653 | |a Wireless networks | ||
| 653 | |a Machine learning | ||
| 653 | |a Internet of Things | ||
| 653 | |a Signal processing | ||
| 653 | |a Data exchange | ||
| 653 | |a Optimization | ||
| 653 | |a Performance degradation | ||
| 653 | |a Errors | ||
| 653 | |a Resource utilization | ||
| 653 | |a Uncertainty | ||
| 700 | 1 | |a Dai, Wei | |
| 700 | 1 | |a Geoffrey Ye Li | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147568270/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.14369 |